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Power Switching Based on Trajectory Planning and Sliding Mode Control for Solid Oxide Fuel Cell Systems

Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1968-1979页

作者： Zhen Wang Guoqiang Liu Xingbo Liu Jie Wang Zhiyang Jin Xiaowei Fu Zhuo Wang Bing Jin Zhonghua Deng Xi Li Key Laboratory of Imaging Processing and Intelligent Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Research Institute of Huazhong University of Science and Technology in Shenzhen ShenzhenChina Hubei Huazhong Electric Power Technology Development Co. Ltd.WuhanChina School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanChinaand he is also with the Wuhan Huamao Automation Co.Ltd.WuhanChina

To improve the safety of the solid oxide fuel cell(SOFC)systems and avoid the generation of large amounts of pollutants during power switching,this paper designs a power switching strategy based on trajectory planning and sliding mode control(TP-SMC).The design elements of the power switching strategy are proposed through simulation analysis at ***,based on the gas transmission delay time and the change of gas flow obtained from testing,trajectory planning(TP)is *** with other power switching strategies,it has been proven that the power switching strategy based on TP has significantly better control ***-thermore,considering the shortcomings and problems of TP in practical application,this paper introduces sliding mode control(SMC)on the basis of TP to improve the power switching *** final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay *** with TP,TP-SMC can ensure that under uncertain conditions,the SOFC system does not experience fuel starvation and temperature exceeding limit during power ***,the NOx emissions are also within the normal and acceptable *** paper can guide the power switching process of the actual SOFC sys-tems to avoid safety issues and excessive generation of NOx,which is very helpful for improving the performance and ser-vice lifeof the SOFCsystems.

关键词： Solid oxide fuel cell(SOFC)system power switching safety NOx emission trajectory planning sliding mode control

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Relation-wise transformer network and reinforcement learning for visual navigation

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Neural Computing and Applications 2024年第21期36卷 13205-13221页

作者： He, Yu Zhou, Kang School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian463000 China Henan Key Laboratory of Smart Lighting Zhumadian463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan463000 China School of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science Wuhan University Wuhan430072 China

The task of object goal navigation is to drive an embodied agent to find the location of a given target only using visual observation. The mapping from visual perception of observation determines the navigation actions. Heterogeneous relationships in the observation are the essential part of the scene graph, which can guide the agent to find the target more easily. In this work, we propose a novel Heterogeneous Zone Graph Visual Transformer formulation for graph representation and visual perception. It consists of two key ideas: (1) Heterogeneous Zone Graph (HZG) that explores the heterogeneous target-related zones graph and spatial information. It allows the agent to navigate efficiently. (2) Relation-wise Transformer Network (RTNet) that transforms the relationship between previously observed objects and navigation actions. RTNet extracts rich nodes and edges features as pays more attention to the target-related zone. We model self-attention on the node-to-node encoder and cross-attention on the edge-to-node decoder. We evaluate our methods on the AI2THOR dataset and show superior navigation performance. Code and datasets can be found inhttps://***/zhoukang12321/RTNet_VN_2023. © The Author(s) 2024.

关键词： Navigation

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A Lightweight Network of Groupwise Separable Convolution and Vision Transformer for Hyperspectral Image Classification 3

A Lightweight Network of Groupwise Separable Convolution and...

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Zhao, Zhuoyi Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Artificial Intelligence and Computer Vision Laboratory University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China

ISBN: (纸本)9781510680319

Hyperspectral image (HSI) data consists of images with numerous contiguous spectral bands and promotes the extensive applications in the field of remote sensing. Recent approaches based on Vision Transformer (ViT) have achieved remarkable performance in HSI classification tasks due to their ability to extract global spatial features and model long-range dependencies. However, ViT has complex network structure, training is challenging and lacks adequate consideration of local spatial and spectral receptive fields in hyperspectral data. To solve the problems above, we propose a lightweight network model known as the Groupwise Separable Convolution and Vision Transformer (GSCViT). Firstly, we introduce a parameter-free attention for spectral calibration (SC). Then, we meticulously design a novel convolutional approach named Groupwise Separable Convolution (GSC), which greatly reduce the number of convolutional kernel parameters and effectively capture local spatial-spectral information in HSI data. In addition, we employ Groupwise Separable Multi-Head Self-Attention (GSSA) to replace the traditional Multi-Head Self-Attention (MSA) in ViT, thus can simultaneously attend to local and global spatial features in HSI with lower computational burden. Experiments on two benchmark HSI datasets demonstrate that our GSCViT model achieves excellent accuracy with relatively small training samples and outperforms some existing HSI classification algorithms. © 2024 SPIE.

关键词： Convolution

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PFed-NS: An Adaptive Personalized Federated Learning Scheme Through Neural Network Segmentation

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IEEE Transactions on computers 2025年第6期74卷 1936-1948页

作者： Liu, Yuchen He, Ligang Zhang, Zhigao Ren, Shenyuan University of Warwick Department of Computer Science CoventryCV4 7AL United Kingdom Wuhan University School of Computer Science Wuhan430072 China China Hubei Luojia Laboratory Wuhan430072 China Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing100044 China

Federated Learning (FL) is typically deployed in a client-server architecture, which makes the Edge-Cloud architecture an ideal backbone for FL. A significant challenge in this setup arises from the diverse data feature distributions across different edge locations (i.e., non-IID data). In response, Personalized Federated Learning (PFL) approaches have been developed. Network segmentation-based PFL is an important approach to achieving PFL, in which the training network is divided into a global segment for server aggregation and a local segment maintained client-side. Existing methods determine the segmentation before the training, and the segmentation remains fixed throughout the PFL training. However, our investigation reveals that model representations vary as PFL progresses and the fixed segmentation may not deliver best performance across various training settings. To address this, we propose PFed-NS, a PFL framework based on adaptive network segmentation. This adaptive segmentation technique is composed of two elements: a mechanism for assessing divergence of clients’ probability density functions constructed from network layers’ outputs, and a model for dynamically establishing divergence thresholds, beyond which server aggregation is deemed detrimental. Further optimization strategies are proposed to reduce the computation and communication costs incurred by divergence modeling. Moreover, we propose a divergence-based BN strategy to optimize BN performance for network segmentation-based PFL. Extensive experiments have been conducted to compare PFed-NS against recent PFL models. The results demonstrate its superiority in enhancing model accuracy and accelerating convergence. © 1968-2012 IEEE.

关键词： Probability density function

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Dynamic motion of quadrupedal robots on challenging terrain:a kinodynamic optimization approach

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Frontiers of Mechanical Engineering 2024年第3期19卷 23-40页

作者： Qi LI Lei DING Xin LUO Engineering Research Center of Hubei Province for Clothing Information Wuhan 430200China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan 430200China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular ***,conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain *** resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion *** this paper,a kinodynamic optimization approach is *** generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain,we formulate motion planning as an optimization problem;jointly treat the foot’s locations,contact forces,and torso motions as decision variables;combine smooth motion and minimal energy consumption as the objective function;and explicitly represent feasible foothold region and friction constraints based on terrain *** track the generated motions accurately and stably,we employ a whole-body controller to compute reference position and velocity commands,which are fed forward to joint controllers of the robot’s *** verify the effectiveness of the developed approach through simulation and on a physical quadruped robot *** show that the quadruped robot can successfully traverse a 30°slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.

关键词： quadrupedal robot kinodynamic planning nonlinear optimization challenging terrain whole-body control

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Continuous Spatiotemporal Events Decoupling through Spike-based Bayesian Computation 38

Continuous Spatiotemporal Events Decoupling through Spike-ba...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zheng, Yajing Zhang, Jiyuan Yu, Zhaofei Huang, Tiejun State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China

Numerous studies have demonstrated that the cognitive processes of the human brain can be modeled using the Bayes theorem for probabilistic inference of the external world. Spiking neural networks (SNNs), capable of performing Bayesian computation with greater physiological interpretability, offer a novel approach to distributed information processing in the cortex. However, applying these models to real-world scenarios to harness the advantages of brain-like computation remains a challenge. Recently, bio-inspired sensors with high dynamic range and ultra-high temporal resolution have been widely used in extreme vision scenarios. Event streams, generated by various types of motion, represent spatiotemporal data. Inferring motion targets from these streams without prior knowledge remains a difficult task. The Bayesian inference-based Expectation-Maximization (EM) framework has proven effective for motion segmentation in event streams, allowing for decoupling without prior information about the motion or its source. This work demonstrates that Bayesian computation based on spiking neural networks can decouple event streams of different motions. The Winner-Take-All (WTA) circuits in the constructed network implement an equivalent E-step, while Spike Timing Dependent Plasticity (STDP) achieves an equivalent optimization in the M-step. Through theoretical analysis and experiments, we show that STDP-based learning can maximize the contrast of warped events under mixed motion models. Experimental results show that the constructed spiking network can effectively segment the motion contained in event streams. © 2024 Neural information processing systems foundation. All rights reserved.

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An Efficient Topology Emulation Technology for the Space-Air-Ground Integrated Network

An Efficient Topology Emulation Technology for the Space-Air...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Wang, Xiaofeng Shen, Taiqian Zhang, Yi Chen, Xinyu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665494274

The space-air-ground integrated network (SAGIN) is a comprehensive and complex network. Network emulation plays an important role in the validation and evaluation of SAGIN's new technologies. Aiming at the characteristics of heterogeneous nodes and the large-scale and dynamic topology of SAGIN, we propose an efficient topology emulation architecture for SAGIN, named SAGINHTE-Stack. To improve usability, scalability and efficiency, we propose three technologies: unified construction technology of the object model, static topology rapid deployment technology and topology dynamic synchronization technology. The experimental data show that the unified construction technology of the object model can complete the uni-fied model construction for various heterogeneous nodes. Static topology rapid deployment technology realizes the construction of the topology model at the millisecond level and completes the rapid deployment of the emulation topology. Topology dynamic synchronization technology realizes the parsing of the topology model change behavior and the synchronization of the topology model;moreover, topology dynamic synchronization technology completes the fast synchronization of the emulation topology. © 2023 IEEE.

关键词： Topology

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Easier Ways to Prove Counting Hard: A Dichotomy for Generalized #SAT, Applied to Constraint Graphs 35

Easier Ways to Prove Counting Hard: A Dichotomy for Generali...

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Demaine, Erik D. Gomez, Timothy Stock, Frederick Brunner, Josh Diomidova, Jenny Hecher, Markus Zhou, Zixiang MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States Miner School of Computer & Information Sciences University of Massachusetts LowellMA United States

ISBN: (纸本)9783959773546

To prove #P-hardness, a single-call reduction from #2SAT needs a clause gadget to have exactly the same number of solutions for all satisfying assignments - no matter how many and which literals satisfy the clause. In this paper, we relax this condition, making it easier to find #P-hardness reductions. Specifically, we introduce a framework called Generalized #SAT where each clause contributes a term to the total count of solutions based on a given function of the literals. For two-variable clauses (a natural generalization of #2SAT), we prove a dichotomy theorem characterizing when Generalized #SAT is in FP versus #P-complete. Equipped with these tools, we analyze the complexity of counting solutions to Constraint Graph Satisfiability (CGS), a framework previously used to prove NP-hardness (and PSPACE-hardness) of many puzzles and games. We prove CGS ASP-hard, meaning that there is a parsimonious reduction (with algorithmic bijection on solutions) from every NP search problem, which implies #P-completeness. Then we analyze CGS restricted to various subsets of features (vertex and edge types), and prove most of them either easy (in FP) or hard (#P-complete). Most of our results also apply to planar constraint graphs. CGS is thus a second powerful framework for proving problems #P-hard, with reductions requiring very few gadgets. © MIT Hardness Group, Josh Brunner, Erik D. Demaine, Jenny Diomidova, Timothy Gomez, Markus Hecher, Frederick Stock, and Zixiang Zhou.

关键词： Brinell Hardness

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Event Contrastive Representation Learning Enhanced with Image Situational Information 12th

Event Contrastive Representation Learning Enhanced with Ima...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Liu, Wei Wu, Qi Xie, Shaorong Li, Weimin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9783031446955

Event representation learning is a crucial prerequisite for many domains in event understanding, which aims to project events into dense vectors, where similar events are embedded close to each other while distinct events are separated. Most existing methods enhanced representation with external knowledge, which only based on unimodal and margin loss, ignoring the diversity of data and the impact of multiple samples. Therefore, we propose a novel Multimodal Event Contrastive Representation Learning (ECRL) framework. ECRL enhances event representations by making better use of image situational information. Specifically, we introduce multimodal contrastive learing and prototype-based clustering methods that allows us to avoid semantic-related samples in same batch being represent as negative, while considering multiple negatives. At the same time, we proposed action-guided-attention mechanism to capture core event participants in image. Extensive experiments on three benchmark datasets of event similarity task show that the ECRL outperforms other baseline. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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RandoMix: a mixed sample data augmentation method with multiple mixed modes

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Multimedia Tools and Applications 2025年第8期84卷 4343-4359页

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior erformance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data augmentation Diversity Mixed-sample RandoMix Robustness

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